By Rob M. Konijn, Wouter Duivesteijn (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)

The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed complaints of the seventeenth Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. the whole of ninety eight papers provided in those court cases used to be conscientiously reviewed and chosen from 363 submissions. They disguise the overall fields of knowledge mining and KDD largely, together with trend mining, category, graph mining, purposes, computing device studying, characteristic choice and dimensionality relief, a number of info assets mining, social networks, clustering, textual content mining, textual content type, imbalanced information, privacy-preserving information mining, suggestion, multimedia information mining, circulate facts mining, information preprocessing and representation.

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S. K. Tanbeer The UH-Mine algorithm [2] stores all frequent items in each DB transaction in a hyper-structure called UH-struct. As UH-Mine does not take advantage of prefix-sharing, the size of the resulting UH-struct is always as large as that of the DB for the frequent items. However, UH-Mine was reported [2,15] to be faster than UFP-growth. 3 Our PUF-Tree Structure To reduce the size of the UF-tree and UFP-tree, we propose the prefix-capped uncertain frequent pattern tree (PUF-tree) structure, in which important information about uncertain data is captured so that frequent patterns can be mined from the tree.

Communications in Statistics - Theory and Methods 26(6), 1481–1496 (1997) 7. : k-nn as an implementation of situation testing for discrimination discovery and prevention. In: Proceedings of KDD 2011, New York, NY, USA, pp. 502–510 (2011) 8. : An algorithm for multi-relational discovery of subgroups. M. ) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997) PUF-Tree: A Compact Tree Structure for Frequent Pattern Mining of Uncertain Data Carson Kai-Sang Leung and Syed Khairuzzaman Tanbeer Dept.

T can be extended to generate new atrees in two different ways. In the first way, a new child N is added to the rightmost node of T (right node extension). In the second way, a new sibling N is added to a node in the rightmost path of T (right path extension) [6]. In the classical approach, N represents every valid node from the input database. In our approach, new nodes N are created from every valid node Q from the input database. , Nk } used for tree extension. Each Ni is associated with an itemset of size 1; the only item being the ith item of λ(Q).

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